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Vulnerability forecasting models help us to predict the number of vulnerabilities that may occur in the future for a given Operating System (OS). There exist few models that focus on quantifying future vulnerabilities without consideration of trend, level, seasonality and non linear components of vulnerabilities. Unlike traditional ones, we propose a vulnerability analytic prediction model based on linear and non-linear approaches via time series analysis. We have developed the models based on Auto Regressive Moving Average (ARIMA), Artificial Neural Network (ANN), and Support Vector Machine (SVM) settings. The best model which provides the minimum error rate is selected for prediction of future vulnerabilities. Utilizing time series approach, this study has developed a predictive analytic model for three popular Desktop Operating Systems, namely, Windows 7, Mac OS X, and Linux Kernel by using their reported vulnerabilities on the National Vulnerability Database (NVD). Based on these reported vulnerabilities, we predict ahead their behavior so that the OS companies can make strategic and operational decisions like secure deployment of OS, facilitate backup provisioning, disaster recovery, diversity planning, maintenance scheduling, etc. Similarly, it also helps in assessing current security risks along with estimation of resources needed for handling potential security breaches and to foresee the future releases of security patches. The proposed non-linear analytic models produce very good prediction results in comparison to linear time series models.

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There are several security metrics developed to protect the computer networks. In general, common security metrics focus on qualitative and subjective aspects of networks lacking formal statistical models. In the present study, we propose a stochastic model to quantify the risk associated with the overall network using Markovian process in conjunction with Common Vulnerability Scoring System (CVSS) framework. The model we developed uses host access graph to represent the network environment. Utilizing the developed model, one can filter the large amount of information available by making a priority list of vulnerable nodes existing in the network. Once a priority list is prepared, network administrators can make software patch decisions. Gaining in depth understanding of the risk and priority level of each host helps individuals to implement decisions like deployment of security products and to design network topologies.

The Himalayan region has already witnessed profound climate changes detectable in the cryosphere and the hydrological cycle, already resulting in drastic socio-economic impacts. We developed a 619-yea-long tree-ring-width chronology from the central Nepal Himalaya, spanning the period 1399–2017 CE. However, due to low replication of the early part of the chronology, only the section after 1600 CE was used for climate reconstruction. Proxy climate relationships indicate that temperature conditions during spring (March–May) are the main forcing factor for tree growth of Tsuga dumosa at the study site. We developed a robust climate reconstruction model and reconstructed spring temperatures for the period 1600–2017 CE. Our reconstruction showed cooler conditions during 1658–1681 CE, 1705–1722 CE, 1753–1773 CE, 1796–1874 CE, 1900–1936 CE, and 1973 CE. Periods with comparably warmer conditions occurred in 1600–1625 CE, 1633–1657 CE, 1682–1704 CE, 1740–1752 CE, 1779–1795 CE, 1936–1945 CE, 1956–1972 CE, and at the beginning of the 21st century. Tropical volcanic eruptions showed only a sporadic impact on the reconstructed temperature. Also, no consistent temperature trend was evident since 1600 CE. Our temperature reconstruction showed positive teleconnections with March–May averaged gridded temperature data for far west Nepal and adjacent areas in Northwest India and on the Southwest Tibetan plateau. We found spectral periodicities of 2.75–4 and 40–65 years frequencies in our temperature reconstruction, indicating that past climate variability in central Nepal might have been influenced by large-scale climate modes, like the Atlantic Multi-decadal Oscillation, the North Atlantic Oscillation, and the El Niño-Southern Oscillation.